
Lecturer in Organisational Psychology @Birkbeck. Passionate about diversity, social justice and open science. 🐘 @lukaswallrich@sciences.social
London
Joined February 2011
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Lukas Wallrich retweeted
Hey, Claude: "I asked an earlier Claude to Remove the Squid from All Quiet on the Western Front. Now you can make movies and such. I need you to show how far you have come" & I pasted in the tweet below
There is some clever stuff, models have a real sense of humor at this point
The programme for the FORRT AI in Metascience conference is live: forrt.org/ai-metascience/
Join 200+ participants on Sept 28–30 to discuss how AI is transforming metascience, research integrity, and open scholarship ... and how we can steer these tools for good.
Lukas Wallrich retweeted
A reviewed letter in a respected academic journal says that LLMs never say "no".
If only we could test this assertion...
AI in science: slop, tool, or unlock? At the FORRT AI in Metascience conference, we'll spend three days discussing the evidence and enabling skill building. 34 talks and workshops, 28-30 Sept, online and free. Register at forrt.org/ai-metascience/
Lukas Wallrich retweeted
It is not inherently bad to publish research on the impact of AI that only refers to older models, but it requires a very careful discussion and has to be very clear to non-technical readers.
If you show AI can do something, its fine. Generally, once AI has gained an ability, it does not regress in future generations. So papers that show that AI has crossed a threshold (such as "good as a human") or that seek to establish some sort of minimum impact or effect still hold up even if they used GPT-4.
If the finding is that AI is bad or biased at something, you need to be much more careful. You cannot claim that because GPT-4 fails at something, that AI is bad at that task, because current or future models may do it. So what can you claim? These are just some examples:
1) You can just be explicit: "GPT-4 could not do X." This is a rare way to frame things because it is not really an interesting publishable paper in many cases. However, if you provide the information needed to reproduce your work, it can become a useful benchmark to measure progress.
2) You can measure trends: compare GPT-4 to GPT-5 to GPT-5.6 Sol or whatever (it is really important to include at least one reasoning model). You can then make better claims about abilities relating to this task.
3) You can make a strong, grounded argument that AI has a natural flaw or limitation that means it cannot do X, and then demonstrate evidence that this might be the case.
4) You can focus on a moderator or mediator: this sort of prompting or approach or social context or connection impacts the ability of GPT-4 to do X, and needs to be a concern for the future.
5) You can focus on humans: how do people react to AI? what are the failures and successes? what dangers or advantages or changes does it bring to us?
Again, these aren't exhaustive, but generally negative capability claims have been much less durable than positive ones.
Lukas Wallrich retweeted
Two stipendiary Research Fellowship positions on the theme of Replication at King's College, Cambridge, UK Shoykd be interesting for metascientists who completed a PhD less than 2 years ago: kings.cam.ac.uk/research/cur…
Lukas Wallrich retweeted
Meta Research Symposium 2026, 8-9 October 2026, TU/e Eindhoven
paulmeehlschool.github.io/wo…
A wonderful event for ECRs doing meta-science.
I'll give a workshop on "How to use AI tools, agents, LLMs, and automation to improve open science practices"
Hope to see you there.
Lukas Wallrich retweeted
This paper gives a fancy name to a problem you can already feel in your bones
"The Tragedy of the Cognitive Commons." Checking AI output requires deep expertise. Deep expertise comes from doing grunt work for years. And grunt work is the first thing AI eats
So we're building systems that need expert supervision while dismantling the only known process for making experts
The paper calls the shared pool of human expertise the "cognitive commons." Every profession drinks from it. Nobody's refilling it.
By eliminating junior roles, each company is acting totally rationally, and the collective result: a profession that can't catch AI's mistakes anymore because it never learned to do the work in the first place
Who checks AI's homework in 15 years?
Lukas Wallrich retweeted
Postdoctoral Associate in Meta-Research at Carnegie Mellon University
cmu.wd5.myworkdayjobs.com/en…
How will AI change science? Really enjoyed discussing that with colleagues at the UKRI Metascience Cohort meeting today, and then wondered what AI models have to say. Fable worked with ChatGPT and Kimi and came up with six rather interesting futures: ai-will-do-science.surge.sh
Lukas Wallrich retweeted
We launched our beta version 0.0.1 of Metacheck today! After our second year of work on the tool, we now have validated modules, hugely expanded functionality, optional (or local) LLM use, checks inside code files, and much more! Try it out in R now: scienceverse.org/metacheck/
Lukas Wallrich retweeted
🚨 New AI Games round is live 🚨
Our first AI Games paper is now out at a top general interest journal (see preprint here:
econstor.eu/bitstream/10419/…
So… we’re launching the next stage 👇
Lukas Wallrich retweeted
I coded up an open-source, not-for-profit AI paper reviewer that rivals the performance of @reviewer3com, @RefineInk, and Stanford Agentic Reviewer (according to @GeminiApp). Costs <$2!
Live @ coarse.ink. Plug in paper, @OpenRouter key, and email. #econtwitter.
Lukas Wallrich retweeted
Advice for PhD students in economics about using AI, from the brilliant Isaiah Andrews. This should probably be circulated to all PhD cohorts
economics.mit.edu/sites/defa…
Lukas Wallrich retweeted
While social media shows you extreme content, AI is more likely to show people more moderate content
This means the technology, on average, could have very different effects on polarization.
ft.com/content/3880176e-d3ac…
In a randomised controlled trial in Edo State, Nigeria, researchers tested a six-week after-school programme where students worked in pairs using GPT-4 under teacher supervision and with prompts designed to promote reasoning rather than shortcuts. voxdev.org/topic/education/h…
Lukas Wallrich retweeted
This feels like cheating.
People pay $30-$50 for custom city map posters on Etsy.
Someone just open-sourced the exact same thing for free.
It's called TerraInk.
Its a cartographic poster engine built on OpenStreetMap data.
Type a city. Customize everything. Download and print.
What you get:
→ Any city on Earth via OpenStreetMap
→ Roads, water, parks, building outlines
→ Full theme and color control
→ Custom fonts via Google Fonts
→ PNG export, print-ready
The whole thing runs in your browser.
No account. No subscription. No checkout.
Self-host it with one Docker command if you want it completely yours.
100% Open Source. MIT License.
Live app: terraink.app
Lukas Wallrich retweeted
The gtsummary package in R makes it easy to create clean, publication-ready summary tables for your data. The tbl_summary() function takes a data set and generates well-organized tables that display key statistics like counts, percentages, means, and medians, which is perfect for presenting descriptive statistics in reports, manuscripts, or presentations.
✔️ Streamlines Descriptive Statistics: Quickly generates clear and professional summary tables.
✔️ Customizable: Offers flexible options for labels, decimal places, and formatting.
✔️ Supports Various Data Types: Works with continuous, categorical, and dichotomous variables seamlessly.
The visualization below shows a sample table created with tbl_summary(), illustrating its ability to organize and present descriptive statistics effectively. Visualization adapted from the official package website: danieldsjoberg.com/gtsummary…
Want to explore more practical tips on Statistics, Data Science, R, and Python? Subscribe to my email newsletter! For more information, visit this link: statisticsglobe.com/newslett…
#Statistical #RStats #Data #Rpackage
Lukas Wallrich retweeted
A great new preprint on the importance of pilot studies for the validity of studies that are performed. Such an important tooic, that is discussed too little. I especially liked the section on the need for transparent reporting. osf.io/t968e_v1 By @YashvinSeetahul et al.




























